Critical care requires rapid decisions from complex, evolving data. Predictive models forecast deterioration but do not guide therapy. Prescriptive AI-causal machine learning (ML) and reinforcement learning (RL)-can bridge this gap. Causal ML estimates individualized treatment effects; RL generates adaptive treatment policies over time. This review examines the shift from prediction to personalization, key barriers, and future pathways including digital twins and clinician‑in‑the‑loop systems.
Some people who suffer a concussion struggle with headaches and fatigue afterwards. Headaches and fatigue follow a consistent pattern that could potentially be used to warn the patient a full day before the symptoms set in.
The patterns are distinct enough for specific types of artificial intelligence models to detect and learn from them. A recent study shows that the models could predict moderate to severe headaches and fatigue with moderate to good accuracy 24 hours before the symptoms occurred.
Dr. Jue Wang, Head of Global Business Development at Insilico Medicine, has been invited to attend the China Healthcare Summit in Shanghai. On the afternoon of November 3 at 16:45, she will deliver a keynote speech in the panel titled: "THE COMMODITIZATION TRAP: WHO WINS WHEN EVERYONE HAS THE SAME AI?"
As generative AI tools become commoditized and model capabilities increasingly turn into "public infrastructure," true differentiation no longer lies in whether one has AI, but in how seamlessly AI is integrated into a full closed-loop workflow-from target discovery and molecular generation to clinical validation. Drawing on Insilico Medicine's engineering practices and external partnerships with the Pharma.AI platform, Dr. Wang will share her perspectives on the competitive landscape in the "era of AI commoditization." She will highlight that data assets, automated experimental feedback loops, expert models, and tangible pipeline deliverables are the true keys to overcoming the commoditization trap. As Head of Global Business Development, she will also provide front-line insights into cross-border transactions and partnering models in AI-driven drug discovery.
Dr Weien Chow, chief data and digital officer at Changi General Hospital, shares how the hospital evolves alongside AI development.
When a patient arrives in the emergency department with severe abdominal pain, every minute counts. Researchers at the University of Colorado Anschutz School of Medicine are investigating ways to streamline the work of radiologists and make it more efficient. They also aim to quickly identify patients who may need urgent surgery and speed up the path to treatment.
The scientists have uncovered a new way to leverage artificial intelligence (AI) to interpret right upper quadrant (RUQ) ultrasounds, which visualize organs in the right upper side of the abdomen such as the liver, the gallbladder, the right kidney, and part of the pancreas.
In a recent study in Nature Communications, researchers created a vision-language AI agent, which blends visual ultrasound data with written clinical data from the lab, that can accurately read RUQ ultrasounds, diagnose common health conditions affecting these organs, and suggest logical next steps.
Harrison Bai, MD, MS, a visiting associate professor in the Department of Radiology and senior author of the paper, emphasizes that this AI agent is designed to work alongside a trained radiologist, generating reports for the radiologist to read and evaluate. It's a tool that can help streamline radiologists' daily work.
"The goal is not to replace the physician. We're not trying to build an AI radiologist," Bai says. "But if our AI agent can write up preliminary reports, it saves radiologists time."
And time is of the essence, especially in the case of a medical emergency: Bai and his team's new AI tool can also rapidly identify patients who may need emergency surgery so they can get to the operating table as quickly as possible.
Artificial intelligence is helping more children's health systems create clinical documentation with ambient listening, and will soon be used to manage claim denials, says Harun Rashid, chief digital information officer at Akron Children's.
A new rapid review in the journal Humanities and Social Sciences Communications explores how digital technologies, digital literacy, and knowledge management shape existential inquiry and meaningful engagement with technology.
A staffer at Bayview Medical Center apparently uploaded a spreadsheet to a public AI website that had not been approved for use, exposing data of more than 3,000 patients.
Among more than 24,000 students surveyed in England, 2.7% reported using general-purpose artificial intelligence (AI) for mental health support. More than half of those who rated their experiences found it helpful, but nearly two-thirds of users wanted additional assistance. The study, published in the journal npj Mental Health Research, examined who used these platforms and what other support they accessed or wanted.
Shu-Ching Tsui, deputy superintendent for admin at Shuang Ho Hospital in Taiwan, shares how the hospital determines AI's value.
Artificial intelligence-enhanced virtual agents that interface with patients are helping medical providers balance multiple challenges, says Ram Rajagopalan, Zoom's head of customer experience for AI.
The Center of Surgical and Medical Intelligence aims to drive artificial intelligence innovation, "from the clinic to the operating room."
